On the Use of Size Modifiers When Referring to Visible Objects

Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011

Volumn , Issue , 2011, Pages 3091-3096

  Mitchell, Margaret ^a   van Deemter, Kees ^a   Reiter, Ehud ^a  

^a UNIVERSITY OF ABERDEEN   (United Kingdom)

Author keywords

referring expression generation; size adjectives; size modifiers; visual features

Indexed keywords

GENERATION ALGORITHM; INDIVIDUAL DIMENSIONS; PHYSICAL DIMENSIONS; REFERRING EXPRESSION GENERATION; REFERRING EXPRESSIONS; SIZE ADJECTIVE; SIZE MODIFIER; TARGET OBJECT; VISUAL CUES; VISUAL FEATURE;

EID: 84859952665     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. Amazon. (2008). Amazon mechanical turk: Artificial artificial intelligence.
2. Bartlett, E. J. (1976). Sizing things up: the acquisition of the meaning of dimensional adjectives. Journal of Child Language, 3(02), 205–219.
3. Bierwisch, M., & Lang, E. (Eds.). (1989). Dimensional adjectives: grammatical structure and conceptual interpretation. New York: Springer-Verlag.
4. Bock, J. K., Irwin, D. E., Davidson, D. J., & Levelt, W. J. M. (2003). Minding the clock. Journal of Memory and Language, 48, 653–685.
5. Brown-Schmidt, S., & Tanenhaus, M. K. (2006). Watching the eyes when talking about size: An investigation of message formulation and utterance planning. Journal of Memory and Language, 54, 592–609.
6. Dale, R., & Reiter, E. (1995). Computational interpretations of the gricean maxims in the generation of referring expressions. Cognitive Science, 19, 233–263.
7. Eilers, R. E., Oller, D. K., & Ellington, J. (1974). The acquisition of word-meaning for dimensional adjectives: the long and short of it. Journal of Child Language, 1(02), 195–204.
8. Ferreira, F., & Swets, B. (2002). How incremental is language production? evidence from the production of utterances requiring the computation of arithmetic sums. Journal of Memory and Learning, 46, 57–84.
9. Friedland, G., Jantz, K., & Rojas, R. (2005). SIOX: simple interactive object extraction in still images. Proceedings of the Seventh IEEE International Symposium on Multimedia, 253–259.
10. Gorniak, P., & Roy, D. (2004). Grounded semantic composition for visual scenes. Journal of Artificial Intelligence Research, 21, 429–470.
11. Griffin, Z. M., & Bock, K. (2000). What the eyes say about speaking. Psychological Science, 11, 274–279.
12. Henderson, J. M., & Ferreira, F. (2004). The interface of language, vision, and action: Eye movements and the visual world. New York, NY: Psychology Press.
13. Hermann, T., & Deutsch, W. (1976). Psychologie der objektbenennung. Bern: Huber Verlag.
14. Herzog, G., & Wazinski, P. (1994). Visual translator: Linking perceptions and natural language descriptions. Artificial Intelligence Review, 8(2/3), 175–187.
15. Kelleher, J., Costello, F., & Genabith, J. van. (2005). Dynamically structuring, updating and interrelating representations of visual and linguistic discourse context. Artificial Intelligence, 167, 62–102.
16. Krahmer, E., van Erk, S., & Verleg, A. (2003). Graph-based generation of referring expressions. Computational Linguistics, 29(1), 53–72.
17. Landau, B. (2001). Perceptual units and their mapping with language. In T. Shipley & P. Kellman (Eds.), From fragments to objects: Segmentation and grouping in vision. The Netherlands: Elsevier.
18. Landau, B., & Jackendoff, R. (1993). “What” and “where” in spatial language and spatial cognition. Behavioral and Brain Sciences, 16, 217–265.
19. Levelt, W. J. M. (1989). Speaking: From intention to articulation. Cambridge, MA: MIT Press.
20. Levelt, W. J. M., Roelofs, A. P. A., & Meyer, A. S. (1999). A theory of lexical access in speech production. Behavioral and Brain Sciences, 22, 1–37.
21. Mojsilović, A. (2005). A computational model for color naming and describing color composition of images. IEEE Transactions of Image Processing, 14(5), 690–699.
22. Morzycki, M. (2009). Degree modification of gradable nouns: size adjectives and adnominal degree morphemes. Natural Language Semantics, 17(2), 175–203.
23. Roy, D., & Reiter, E. (2005). Connecting language to the world. Artificial Intelligence, 167, 1–12.
24. Roy, D. K. (2002). Learning visually-grounded words and syntax for a scene description task. Computer Speech and Language, 16, 353–385.
25. Roy, D. K., & Pentland, A. (2002). Learning words from sights and sounds: A computational model. Cognitive Science, 26, 113–146.
26. Sedivy, J. C. (2003). Pragmatic versus form-based accounts of referential contrast: Evidence for effects of informativity expectations. Journal of Psycholinguistic Research, 32, 3–23.
27. Sedivy, J. C., Tanenhaus, M., Chambers, C., & Carlson, G. (1999). Achieving incremental semantic interpretation through contextual representation. Cognition, 71, 109–147.
28. Skočaj, D., Berginc, G., Ridge, B., Vanek, O., Hutter, M., & Hewes, N. (2007). A system for continuous learning of visual concepts. Proceedings of the International Conference on Computer Vision Systems.
29. Tanenhaus, M. K., Spivey-Knowlton, M. J., Eberhard, K. M., & Sedivy, J. C. (1995). Integration of visual and linguistic information in spoken language comprehension. Science, 268, 1632–1634.
30. Tucker, G. (1998). The lexicogrammar of adjectives: A systemic functional approach to lexis. London: Cassell.
31. van Deemter, K. (2000). Generating vague descriptions. Proceedings of the First Natural Language Generation Conference, 12–16.
32. van Deemter, K. (2004). Generating referring expressions that involve gradable properties. Computational Linguistics, 32(2), 195–222.
33. Viethen, J., & Dale, R. (2008). The use of spatial relations in referring expression generation. Proceedings of the Fifth International Natural Language Generation Conference, 59–67.
34. Zheng, S., Yuille, A., & Tu, Z. (2010). Detecting object boundaries using low-, middle-, and high-level information. Journal of Computer Vision and Image Understanding, 114(19), 1055–1067.